I started working on this because no single AI model is right all the time, but their errors don’t strongly correlate. In other words, models often make unique mistakes relative to other models. So I run multiple models in parallel and synthesize the outputs by weighting segments based on confidence. Low entropy in the output token probability distributions correlates with accuracy. High entropy is often where hallucinations begin.

My dad Scott (AI Research Scientist at TRI) is my research partner on this. He sends me papers at all hours, we argue about whether they actually apply and what modifications make sense, and then I build and test things. The entropy-weighting approach came out of one of those conversations.

In our eval on Humanity's Last Exam, Sup scored 52.15%. The best individual model in the same evaluation run got 44.74%. The relative gap is statistically significant (p < 0.001).

Methodology, eval code, data, and raw results:

- https://sup.ai/research/hle-white-paper-jan-9-2026

- https://github.com/supaihq/hle

Limitations:

- We evaluated 1,369 of the 2,500 HLE questions (details in the above links)

- Not all APIs expose token logprobs; we use several methods to estimate confidence when they don't

We tried offering free access and it got abused so badly it nearly killed us. Right now the sustainable option is a $5 starter credit with card verification (no auto-charge). If you don't want to sign up, drop a prompt in the comments and I'll run it myself and post the result.

Try it at https://sup.ai. My dad Scott (@scottmu) is in the thread too. Would love blunt feedback, especially where this really works for you and where it falls short.

Here's a short demo video: https://www.youtube.com/watch?v=DRcns0rRhsg